An illustrative application of generalized structured component analysis for brain connectivity research

Kwanghee Jung; Sang Soo Cho; Jaehoon Lee; Seungman Kim; Ji Hoon Ryoo

doi:10.1007/s41237-019-00080-w

An illustrative application of generalized structured component analysis for brain connectivity research

Kwanghee Jung, Sang Soo Cho, Jaehoon Lee, Seungman Kim, Ji Hoon Ryoo

Department of Education

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

Abstract

Generalized structured component analysis (GSCA) has been extensively enhanced in terms of data-analytic capability and flexibility as well as computational efficiency. This article illustrates a novel application of GSCA for brain connectivity research, the purpose of which is to facilitate its uses with functional neuroimaging data among applied researchers and practitioners. Using data collected during encoding of source memory in a functional magnetic resonance imaging study, this article demonstrates how to specify and evaluate a fully and bidirectionally connected structural model of brain connectivity using GSCA. Implications of the GSCA approach and future directions for brain research are discussed.

Original language	English
Pages (from-to)	273-289
Number of pages	17
Journal	Behaviormetrika
Volume	47
Issue number	1
DOIs	https://doi.org/10.1007/s41237-019-00080-w
Publication status	Published - 2020 Jan 1

Bibliographical note

Publisher Copyright:
© 2019, The Behaviormetric Society.

All Science Journal Classification (ASJC) codes

Analysis
Applied Mathematics
Clinical Psychology
Experimental and Cognitive Psychology

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AU - Ryoo, Ji Hoon

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N2 - Generalized structured component analysis (GSCA) has been extensively enhanced in terms of data-analytic capability and flexibility as well as computational efficiency. This article illustrates a novel application of GSCA for brain connectivity research, the purpose of which is to facilitate its uses with functional neuroimaging data among applied researchers and practitioners. Using data collected during encoding of source memory in a functional magnetic resonance imaging study, this article demonstrates how to specify and evaluate a fully and bidirectionally connected structural model of brain connectivity using GSCA. Implications of the GSCA approach and future directions for brain research are discussed.

AB - Generalized structured component analysis (GSCA) has been extensively enhanced in terms of data-analytic capability and flexibility as well as computational efficiency. This article illustrates a novel application of GSCA for brain connectivity research, the purpose of which is to facilitate its uses with functional neuroimaging data among applied researchers and practitioners. Using data collected during encoding of source memory in a functional magnetic resonance imaging study, this article demonstrates how to specify and evaluate a fully and bidirectionally connected structural model of brain connectivity using GSCA. Implications of the GSCA approach and future directions for brain research are discussed.

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An illustrative application of generalized structured component analysis for brain connectivity research

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

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